README.md

Bandit Environments

Series of n-armed bandit environments for the OpenAI Gym

Environments

• BanditTwoArmedDeterministicFixed-v0: Simplest case where one bandit always pays, and the other always doesn't
• BanditTwoArmedHighLowFixed-v0: Stochastic version with a large difference between which bandit pays out of two choices
• BanditTwoArmedHighHighFixed-v0: Stochastic version with a small difference between which bandit pays where both are good
• BanditTwoArmedLowLowFixed-v0: Stochastic version with a small difference between which bandit pays where both are bad
• BanditTwoArmedUniform-v0: Stochastic version both arms pay between 0 and 1
• BanditTenArmedRandomFixed-v0: 10 armed bandit with random probabilities assigned to payouts
• BanditTenArmedRandomRandom-v0: 10 armed bandit with random probabilities assigned to both payouts and rewards
• BanditTenArmedUniformDistributedReward-v0: 10 armed bandit with that always pays out with a reward selected from a uniform distribution
• BanditTenArmedGaussian-v0: 10 armed bandit mentioned on page 30 of Reinforcement Learning: An Introduction (Sutton and Barto)

Installation

git clone [email protected]:mimoralea/gym-bandits.git
cd gym-bandits
pip install .

or:

pip install git+https://github.com/mimoralea/gym-bandits#egg=gym-bandits

In your gym environment

import gym, gym_bandits
env = gym.make("BanditTenArmedGaussian-v0")